Split Means
Catherine Walsh
3/13/2020
When we plotted omnibus span vs the load effects from the delay period regions, there seems to be a non-linear relationship. Here, we want to investigate this further.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(reshape2)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(patchwork)
library(rockchalk)##
## Attaching package: 'rockchalk'## The following object is masked from 'package:dplyr':
##
## summarizeload('data/load_effects_DFR.RData')
load('data/behav.RData')
load('data/structural_measures.RData')
load('data/connectivity_data.RData')
source("split_into_groups.R")
source("prep_split_for_bar_plots.R")
source("plot_bars.R")constructs_fMRI <- construct_vars_omnibus[construct_vars_omnibus$PTID %in% p200_indiv_ROI_DFR_delay$PTID,]
data_for_plot <- merge(p200_indiv_ROI_DFR_delay,constructs_fMRI)
p1 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_ROIs))+
geom_point()+
stat_smooth(method="loess")
p2 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_dlPFC))+
geom_point()+
stat_smooth(method="loess")
p3 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_IPS))+
geom_point()+
stat_smooth(method="loess")
p4 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_preSMA))+
geom_point()+
stat_smooth(method="loess")
(p1+p2)/(p3+p4)
The way we chose to do this was to split our data into three groups based on capacity score, so we had a low WM capacity group, a medium WM capacity group, and a high WM capacity group. We created these groups by ordering our subjects by capacity score, then making three evenly sized groups of 56 subjects. We drop 2 subjects at the borders of groups to give a slight separation between groups.
#sort by omnibus span
p200_constructs_sorted <- constructs_fMRI[order(constructs_fMRI$omnibus_span_no_DFR_MRI),]
low_WM <- p200_constructs_sorted[1:56,]
med_WM <- p200_constructs_sorted[58:113,]
high_WM <-p200_constructs_sorted[115:170,]
low_WM$level <- "low"
med_WM$level <- "med"
high_WM$level <- "high"
things_to_hist <- rbind(low_WM,med_WM,high_WM)
WM_groups <- list(high=high_WM,med=med_WM, low=low_WM, all=things_to_hist)
save(list=c("WM_groups", "p200_constructs_sorted"),file="data/split_groups_info.RData")split_constructs <- split_into_groups(constructs_fMRI[1:7],WM_groups)
split_clinical <- split_into_groups(p200_clinical_zscores, WM_groups)
split_DFR_delay <- split_into_groups(p200_indiv_ROI_DFR_delay, WM_groups)
split_DFR_cue <- split_into_groups(p200_indiv_ROI_DFR_cue, WM_groups)
split_DFR_probe <- split_into_groups(p200_indiv_ROI_DFR_probe, WM_groups)
split_DFR_FFA <- split_into_groups(p200_FFA,WM_groups)
split_DFR_HPC_Ant <- split_into_groups(p200_HPC_Ant, WM_groups)
split_DFR_HPC_Med <- split_into_groups(p200_HPC_Med, WM_groups)
split_DFR_HPC_Post <- split_into_groups(p200_HPC_Post, WM_groups)
split_fullMask_delay <- split_into_groups(p200_DFR_full_mask, WM_groups)
split_cue_ROIs <- split_into_groups(p200_indiv_ROI_delayDFR_cuePeriod, WM_groups)
split_demographics <- split_into_groups(p200_demographics,WM_groups)
split_cortical_thickness_DFR <- split_into_groups(p200_DFR_fullMask_cortical_thickness,WM_groups)
split_RS <- split_into_groups(p200_all_RS,WM_groups)
split_beta_conn_cue <- split_into_groups(p200_beta_conn_cue,WM_groups)
split_beta_conn_delay <- split_into_groups(p200_beta_conn_delay,WM_groups)
split_BCT <- split_into_groups(p200_BCT_forCorr,WM_groups)
split_indiv_partic_coeff <- split_into_groups(p200_indiv_network_ParticCoeff,WM_groups)save(list=c("split_constructs","split_clinical","split_DFR_delay", "split_DFR_cue", "split_DFR_probe", "split_DFR_FFA", "split_DFR_HPC_Ant", "split_DFR_HPC_Med", "split_DFR_HPC_Post", "split_fullMask_delay", "split_cue_ROIs", "split_demographics","split_cortical_thickness_DFR","split_RS","split_beta_conn_cue","split_beta_conn_delay","split_BCT", "split_indiv_partic_coeff"), file="data/split_WM_groups_fMRI.RData")split_means_demo <- data.frame(matrix(nrow=length(split_demographics)-1,ncol=8))
colnames(split_means_demo) <- c("Trio","Prisma","CS","NCS","female","male","age","age_se")
rownames(split_means_demo) <- names(split_demographics)[1:length(names(split_demographics))-1]
for (level in seq.int(1,length(split_demographics)-1)){
split_means_demo$Trio[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==1])
split_means_demo$Prisma[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==2])
split_means_demo$CS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==1])
split_means_demo$NCS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==2])
split_means_demo$female[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==2])
split_means_demo$male[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==1])
split_means_demo$age[level] <- mean(split_demographics[[level]]$AGE,na.rm=TRUE)
split_means_demo$age_se[level] <- sd(split_demographics[[level]]$AGE,na.rm=TRUE)/sqrt(length(split_demographics[[level]]$AGE[!is.na(split_demographics[[level]]$AGE)]))
}
split_means_demo$level <- as.factor(c("high", "med","low"))
means_melt_demo <- melt(split_means_demo,id.vars="level")Demographics
age_plot <- ggplot(data=split_means_demo,aes(x=level,y=age))+
geom_bar(stat="identity",width = .5, color = "#667Ea4", fill = "#667Ea4")+
geom_errorbar(aes(ymin=age-age_se,ymax=age+age_se),width=.2)+
ggtitle("Age") +
ylab("Mean +/- SE") +
scale_x_discrete(limits = c("low","med","high")) +
theme(aspect.ratio = 1)
scanner_data <- demo_plot_data.m[demo_plot_data.m$variable=="scanner_count",c(1,2,5,6)]
scanner_data$value <- scanner_data$value/56*100
scanner_plot <- ggplot(scanner_data,aes(x=level,y=value,fill=scanner))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Scanner")
gender_data <- demo_plot_data.m[demo_plot_data.m$variable=="gender_count",c(1,3,5,6)]
gender_data$value <- gender_data$value/56*100
gender_plot <- ggplot(gender_data,aes(x=level,y=value, fill=gender))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Gender")
care_data <- demo_plot_data.m[demo_plot_data.m$variable=="care_count",c(1,4:6)]
care_data$value <- care_data$value/56*100
care_plot <- ggplot(care_data,aes(x=level,y=value, fill=care))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("CS vs NCS")
(age_plot + gender_plot)/(care_plot + scanner_plot)+
plot_annotation(title="Demographics split by capacity group")
melt_constructs <- prep_split_for_bar_plots(WM_groups)
melt_clinical <- prep_split_for_bar_plots(split_clinical)
melt_DFR_delay <- prep_split_for_bar_plots(split_DFR_delay)
melt_DFR_cue <- prep_split_for_bar_plots(split_DFR_cue)
melt_DFR_probe <- prep_split_for_bar_plots(split_DFR_probe)
melt_DFR_FFA <- prep_split_for_bar_plots(split_DFR_FFA)
melt_DFR_HPC_Ant <- prep_split_for_bar_plots(split_DFR_HPC_Ant)
melt_DFR_HPC_Med <- prep_split_for_bar_plots(split_DFR_HPC_Med)
melt_DFR_HPC_Post <- prep_split_for_bar_plots(split_DFR_HPC_Post)
melt_fullMask_delay <- prep_split_for_bar_plots(split_fullMask_delay)
melt_cue_ROIs <- prep_split_for_bar_plots(split_cue_ROIs)
melt_cortical_thickness_DFR <- prep_split_for_bar_plots(split_cortical_thickness_DFR)
melt_RS <- prep_split_for_bar_plots(split_RS)
melt_beta_conn_cue <- prep_split_for_bar_plots(split_beta_conn_cue)
melt_beta_conn_delay <- prep_split_for_bar_plots(split_beta_conn_delay)
melt_BCT <- prep_split_for_bar_plots(split_BCT)
melt_indiv_partic_coeff <- prep_split_for_bar_plots(split_indiv_partic_coeff)constructs_plots <- plot_bars(melt_constructs)
clinical_plots <- plot_bars(melt_clinical)
DFR_delay_plots <- plot_bars(melt_DFR_delay)
DFR_cue_plots <- plot_bars(melt_DFR_cue)
DFR_probe_plots <- plot_bars(melt_DFR_probe)
DFR_FFA_plots <- plot_bars(melt_DFR_FFA)
DFR_HPC_Ant_plots <- plot_bars(melt_DFR_HPC_Ant)
DFR_HPC_Med_plots <- plot_bars(melt_DFR_HPC_Med)
DFR_HPC_Post_plots <- plot_bars(melt_DFR_HPC_Post)
fullMask_delay_plots <- plot_bars(melt_fullMask_delay)
cue_ROIs_plots <- plot_bars(melt_cue_ROIs)
cortical_thickness_plots <- plot_bars(melt_cortical_thickness_DFR)
RS_plots <- plot_bars(melt_RS)
beta_conn_cue_plots <- plot_bars(melt_beta_conn_cue)
beta_conn_delay_plots <- plot_bars(melt_beta_conn_delay)
BCT_plots <- plot_bars(melt_BCT)
indiv_partic_coeff_plots <- plot_bars(melt_indiv_partic_coeff)Constructs
It’s a nice sanity check here that all of our constructs hang nicely with our split on span. Individuals with higher span show higher intelligence and LTM.
constructs_plots[["omnibus_span_no_DFR_MRI"]]$labels$title = "Omnibus Span"
(constructs_plots[["omnibus_span_no_DFR_MRI"]]+constructs_plots[["intelligence"]]+constructs_plots[["LTM"]]) +
plot_annotation(title="Constructs split on WM span")
print("Omnibus Span")## [1] "Omnibus Span"span.aov <- aov(omnibus_span_no_DFR_MRI ~ level, data=split_constructs[["all"]])
summary(span.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 39.56 19.781 326.3 <2e-16 ***
## Residuals 165 10.00 0.061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(span.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = omnibus_span_no_DFR_MRI ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.5797433 -0.6897959 -0.4696907 0
## low-high -1.1885516 -1.2986042 -1.0784990 0
## low-med -0.6088083 -0.7188609 -0.4987557 0print("LTM")## [1] "LTM"LTM.aov <- aov(LTM ~ level, data=split_constructs[["all"]])
summary(LTM.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 9.32 4.661 9.152 0.000172 ***
## Residuals 161 81.99 0.509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingnessTukeyHSD(LTM.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LTM ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.3948699 -0.7168413 -0.07289843 0.0117405
## low-high -0.5667132 -0.8886847 -0.24474182 0.0001500
## low-med -0.1718434 -0.4967286 0.15304186 0.4249433print("Intelligence")## [1] "Intelligence"intelligence.aov <- aov(intelligence ~ level, data=split_constructs[["all"]])
summary(intelligence.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 27.47 13.737 32.35 1.41e-12 ***
## Residuals 165 70.07 0.425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(intelligence.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = intelligence ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.4277286 -0.7189938 -0.1364634 0.0018915
## low-high -0.9876024 -1.2788676 -0.6963372 0.0000000
## low-med -0.5598738 -0.8511390 -0.2686086 0.0000312Clinical
There’s a negative relationship if we look at the clinical measures split by span – the low WM capacity is significantly higher scores on BPRS than high capacity subjects (with the same numerical trend in WHODAS).
data_for_plot <- merge(p200_clinical,constructs_fMRI,by="PTID")
ggplot(data=data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = WHO_ST_S32))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=1,y=50,label="r=-0.18*")+
ggtitle("Correlation of WHODAS and Omnibus WM Capacity")
print("WHODAS")## [1] "WHODAS"print(cor.test(data_for_plot$omnibus_span_no_DFR_MRI,data_for_plot$WHO_ST_S32))##
## Pearson's product-moment correlation
##
## data: data_for_plot$omnibus_span_no_DFR_MRI and data_for_plot$WHO_ST_S32
## t = -2.3071, df = 168, p-value = 0.02227
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3173816 -0.0253941
## sample estimates:
## cor
## -0.1752386ggplot(data=data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = BPRS_TOT))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=1,y=70,label="r=-0.25***")+
ggtitle("Correlation of BPRS and Omnibus WM Capacity")
print("BPRS")## [1] "BPRS"print(cor.test(data_for_plot$omnibus_span_no_DFR_MRI,data_for_plot$BPRS_TOT))##
## Pearson's product-moment correlation
##
## data: data_for_plot$omnibus_span_no_DFR_MRI and data_for_plot$BPRS_TOT
## t = -3.387, df = 168, p-value = 0.0008802
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3885498 -0.1063539
## sample estimates:
## cor
## -0.2528211clinical_plots[["WHO_ST_S32"]]$labels$title <- "WHODAS"
clinical_plots[["BPRS"]]$labels$title <- "BPRS"
(clinical_plots[["WHO_ST_S32"]] + clinical_plots[["BPRS_TOT"]])+
plot_annotation(title="Clinical measures split by omnibus span")
print("WHODAS")## [1] "WHODAS"WHODAS.aov <- aov(WHO_ST_S32 ~ level, data=split_clinical[["all"]])
summary(WHODAS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.13 1.5639 1.607 0.204
## Residuals 165 160.57 0.9731print("BPRS")## [1] "BPRS"BPRS.aov <- aov(BPRS_TOT ~ level, data=split_clinical[["all"]])
summary(BPRS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 7.44 3.719 3.895 0.0222 *
## Residuals 165 157.54 0.955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(BPRS.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = BPRS_TOT ~ level, data = split_clinical[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.05063299 -0.38609793 0.4873639 0.9594129
## low-high 0.46950593 0.03277501 0.9062369 0.0318214
## low-med 0.41887294 -0.01785799 0.8556039 0.0631758Cue Period
Full mask
The cue period full mask activity isn’t quite a U-shaped relationship - it’s more asymptotic, with a significantly higher activity in medium vs low capacity subjects.
fullMask_delay_plots[["cue_low"]]+fullMask_delay_plots[["cue_high"]]+fullMask_delay_plots[["cue_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during cue period")
print("Load Effect")## [1] "Load Effect"cue_LE.aov <- aov(cue_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.246 0.6228 4.108 0.0181 *
## Residuals 165 25.012 0.1516
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(cue_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = cue_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.02118365 -0.1528336 0.195200930 0.9553489
## low-high -0.17113697 -0.3451543 0.002880312 0.0550234
## low-med -0.19232062 -0.3663379 -0.018303336 0.0263264Individual ROIs
However, if we look at the individual ROIs, we see that none of them have significant effects.
DFR_cue_plots[["L_FEF_low"]] + DFR_cue_plots[["L_FEF_high"]] + DFR_cue_plots[["L_FEF_loadEffect"]]
DFR_cue_plots[["L_insula_low"]] + DFR_cue_plots[["L_insula_high"]] + DFR_cue_plots[["L_insula_loadEffect"]]
DFR_cue_plots[["L_IPS_low"]] + DFR_cue_plots[["L_IPS_high"]] + DFR_cue_plots[["L_IPS_loadEffect"]]
DFR_cue_plots[["R_FEF_low"]] + DFR_cue_plots[["R_FEF_high"]] + DFR_cue_plots[["R_FEF_loadEffect"]]
DFR_cue_plots[["R_insula_low"]] + DFR_cue_plots[["R_insula_high"]] + DFR_cue_plots[["R_insula_loadEffect"]]
DFR_cue_plots[["R_IPS_low"]] + DFR_cue_plots[["R_IPS_high"]] + DFR_cue_plots[["R_IPS_loadEffect"]]
DFR_cue_plots[["R_MFG_low"]] + DFR_cue_plots[["R_MFG_high"]] + DFR_cue_plots[["R_MFG_loadEffect"]]
DFR_cue_plots[["R_preSMA_low"]] + DFR_cue_plots[["R_preSMA_high"]] + DFR_cue_plots[["R_preSMA_loadEffect"]]
print("L FEF")## [1] "L FEF"cue_L_FEF.aov <- aov(L_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_FEF.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.03 0.01472 0.065 0.937
## Residuals 165 37.51 0.22731print("L insula")## [1] "L insula"cue_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.69 0.3428 1.306 0.274
## Residuals 165 43.29 0.2624print("L IPS")## [1] "L IPS"cue_L_IPS.aov <- aov(L_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.62 0.3076 0.725 0.486
## Residuals 165 70.05 0.4246print("L occipital")## [1] "L occipital"cue_L_occipital.aov <- aov(L_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_occipital.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.51 0.2535 0.534 0.587
## Residuals 165 78.37 0.4750print("R FEF")## [1] "R FEF"cue_R_FEF.aov <- aov(R_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_FEF.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.31 1.1527 2.104 0.125
## Residuals 165 90.40 0.5479print("R insula")## [1] "R insula"cue_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.55 0.2771 1.257 0.287
## Residuals 165 36.37 0.2204print("R IPS")## [1] "R IPS"cue_R_IPS.aov <- aov(R_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.23 0.1127 0.282 0.755
## Residuals 165 66.04 0.4002print("R MFG")## [1] "R MFG"cue_R_MFG.aov <- aov(R_MFG_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_MFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.049 0.02436 0.128 0.88
## Residuals 165 31.476 0.19076print("R occipital")## [1] "R occipital"cue_R_occipital.aov <- aov(R_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_occipital.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.29 0.1462 0.327 0.722
## Residuals 165 73.83 0.4475print("R preSMA")## [1] "R preSMA"cue_R_preSMA.aov <- aov(R_preSMA_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_preSMA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.91 0.4570 0.986 0.375
## Residuals 165 76.51 0.4637Delay Period
Full Mask
During the delay period, we see that there is significant differences btween low and medium, with trending differences between medium and high in the load effect, and the same numerical trend in the high load condition.
fullMask_delay_plots[["delay_low"]]+fullMask_delay_plots[["delay_high"]]+fullMask_delay_plots[["delay_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during delay period")
print("Low Load")## [1] "Low Load"delay_L1.aov <- aov(delay_low ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L1.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.096 0.04782 1.537 0.218
## Residuals 165 5.133 0.03111TukeyHSD(delay_L1.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_low ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.02200539 -0.10083520 0.05682442 0.7868335
## low-high 0.03588517 -0.04294464 0.11471498 0.5299027
## low-med 0.05789056 -0.02093925 0.13672037 0.1946256print("High Load")## [1] "High Load"delay_L3.aov <- aov(delay_high ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.207 0.10334 2.85 0.0607 .
## Residuals 165 5.983 0.03626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(delay_L3.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_high ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.070617603 -0.01449341 0.15572861 0.1248899
## low-high -0.007068737 -0.09217975 0.07804227 0.9789572
## low-med -0.077686340 -0.16279735 0.00742467 0.0814790print("Load Effect")## [1] "Load Effect"delay_LE.aov <- aov(delay_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.538 0.26885 5.611 0.00439 **
## Residuals 165 7.905 0.04791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(delay_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.09262299 -0.00520811 0.19045409 0.0676813
## low-high -0.04295391 -0.14078501 0.05487719 0.5537589
## low-med -0.13557690 -0.23340800 -0.03774580 0.0036368Individual ROIs
This inverted U-shaped pattern follows in all of the individual ROIs except for the R dMFG.
(DFR_delay_plots[["DFR_L_aMFG"]] + DFR_delay_plots[["DFR_L_dlPFC"]] + DFR_delay_plots[["DFR_L_dMFG"]]) + plot_annotation(title="individual DFR delay period ROIs")
(DFR_delay_plots[["DFR_L_IPS"]] + DFR_delay_plots[["DFR_L_preSMA"]] + DFR_delay_plots[["DFR_R_dlPFC"]]) 
(DFR_delay_plots[["DFR_R_dMFG"]] + DFR_delay_plots[["DFR_R_IPS"]] + DFR_delay_plots[["DFR_R_medParietal"]]) 
print("L aMFG")## [1] "L aMFG"L_aMFG.aov <- aov(DFR_L_aMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_aMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.058 0.5292 6.543 0.00184 **
## Residuals 165 13.346 0.0809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_aMFG.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_aMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.1055906 -0.02152261 0.23270391 0.1242955
## low-high -0.0885831 -0.21569636 0.03853015 0.2285217
## low-med -0.1941738 -0.32128701 -0.06706049 0.0011640print("L dlPFC")## [1] "L dlPFC"L_dlPFC.aov <- aov(DFR_L_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(L_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.504 0.25184 3.502 0.0324 *
## Residuals 165 11.866 0.07191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_dlPFC.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_dlPFC ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.08103970 -0.03881938 0.20089879 0.2489147
## low-high -0.05203218 -0.17189127 0.06782691 0.5611047
## low-med -0.13307188 -0.25293097 -0.01321279 0.0254909print("L dMFG")## [1] "L dMFG"L_dMFG.aov <- aov(DFR_L_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_dMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.350 0.17517 3.241 0.0416 *
## Residuals 165 8.917 0.05404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_dMFG.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_dMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.093992297 -0.009913502 0.197898096 0.0851170
## low-high -0.005521105 -0.109426905 0.098384694 0.9913316
## low-med -0.099513402 -0.203419202 0.004392397 0.0636677print("L IPS")## [1] "L IPS"L_IPS.aov <- aov(DFR_L_IPS ~ level, data=split_DFR_delay[["all"]])
summary(L_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.726 0.3631 7.301 0.000915 ***
## Residuals 165 8.205 0.0497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_IPS.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.12567939 0.02600744 0.22535133 0.0091960
## low-high -0.02436776 -0.12403971 0.07530418 0.8319470
## low-med -0.15004715 -0.24971909 -0.05037521 0.0013991print("L preSMA")## [1] "L preSMA"L_preSMA.aov <- aov(DFR_L_preSMA ~ level, data=split_DFR_delay[["all"]])
summary(L_preSMA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.691 0.3453 5.959 0.00317 **
## Residuals 165 9.560 0.0579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_preSMA.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_preSMA ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.07238329 -0.03520077 0.17996734 0.2522816
## low-high -0.08450406 -0.19208812 0.02307999 0.1544039
## low-med -0.15688735 -0.26447141 -0.04930330 0.0020544print("R dlPFC")## [1] "R dlPFC"R_dlPFC.aov <- aov(DFR_R_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(R_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.695 0.3473 4.584 0.0115 *
## Residuals 165 12.498 0.0757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_dlPFC.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_dlPFC ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.09681577 -0.02619647 0.21982802 0.1532731
## low-high -0.05917114 -0.18218338 0.06384111 0.4923528
## low-med -0.15598691 -0.27899916 -0.03297466 0.0087390print("R dMFG")## [1] "R dMFG"R_dMFG.aov <- aov(DFR_R_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(R_dMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.246 0.12303 1.776 0.173
## Residuals 165 11.430 0.06927TukeyHSD(R_dMFG.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_dMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.06928517 -0.04835026 0.18692059 0.3469096
## low-high -0.02004342 -0.13767885 0.09759200 0.9144349
## low-med -0.08932859 -0.20696401 0.02830683 0.1741041print("R IPS")## [1] "R IPS"R_IPS.aov <- aov(DFR_R_IPS ~ level, data=split_DFR_delay[["all"]])
summary(R_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.635 0.3176 4.339 0.0146 *
## Residuals 165 12.076 0.0732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_IPS.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.08920070 -0.03171401 0.21011540 0.1917863
## low-high -0.06049554 -0.18141024 0.06041917 0.4648133
## low-med -0.14969623 -0.27061094 -0.02878153 0.0108140print("R medial Parietal")## [1] "R medial Parietal"R_medParietal.aov <- aov(DFR_R_medParietal ~ level, data=split_DFR_delay[["all"]])
summary(R_medParietal.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.40 0.6978 3.512 0.0321 *
## Residuals 165 32.79 0.1987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_medParietal.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_medParietal ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.17810209 -0.02114231 0.377346488 0.0900794
## low-high -0.02754366 -0.22678806 0.171700738 0.9428016
## low-med -0.20564575 -0.40489015 -0.006401351 0.0413521Probe Period
Full Mask
During the probe period, we see a similar asymptotic pattern as in the cue period in the load effects - medium capacity have significantly different load effects vs low capacity.
fullMask_delay_plots[["probe_low"]]+fullMask_delay_plots[["probe_high"]]+fullMask_delay_plots[["probe_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during probe period")
print("Low Load")## [1] "Low Load"probe_L1.aov <- aov(probe_low ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L1.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.54 0.2693 0.579 0.561
## Residuals 165 76.69 0.4648TukeyHSD(probe_L1.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = probe_low ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1196399517 -0.4243437 0.1850638 0.6229979
## low-high 0.0009300794 -0.3037737 0.3056338 0.9999713
## low-med 0.1205700311 -0.1841337 0.4252738 0.6184348print("High Load")## [1] "High Load"probe_L3.aov <- aov(probe_high ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.14 1.5705 2.296 0.104
## Residuals 165 112.87 0.6841TukeyHSD(probe_L3.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = probe_high ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.05022285 -0.4198880 0.31944231 0.9446982
## low-high -0.31188633 -0.6815515 0.05777884 0.1165272
## low-med -0.26166347 -0.6313286 0.10800169 0.2182263print("Load Effect")## [1] "Load Effect"probe_LE.aov <- aov(probe_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(probe_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.64 2.3219 3.717 0.0264 *
## Residuals 165 103.08 0.6248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(probe_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = probe_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.0694171 -0.2838614 0.42269561 0.8878729
## low-high -0.3128164 -0.6660949 0.04046211 0.0941450
## low-med -0.3822335 -0.7355120 -0.02895499 0.0304793Individual ROIs
However, none of the individual ROIs follow that pattern.
DFR_probe_plots[["dmPFC_loadEffect"]] + DFR_probe_plots[["L_aMFG_loadEffect"]] + DFR_probe_plots[["L_dlPFC_loadEffect"]] +
plot_annotation(title="individual DFR activity from probe period regions")
DFR_probe_plots[["L_insula_loadEffect"]] + DFR_probe_plots[["L_IPS_loadEffect"]] + DFR_probe_plots[["R_dlPFC_loadEffect"]] 
DFR_probe_plots[["R_insula_loadEffect"]] + DFR_probe_plots[["R_OFC_loadEffect"]] 
print("dmPFC")## [1] "dmPFC"probe_dmPFC.aov <- aov(dmPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_dmPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.96 0.4804 1.62 0.201
## Residuals 165 48.91 0.2965print("L aMFG")## [1] "L aMFG"probe_L_aMFG.aov <- aov(L_aMFG_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_aMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.05 0.5233 0.757 0.471
## Residuals 165 114.06 0.6913print("L dlPFC")## [1] "L dlPFC"probe_L_dlPFC.aov <- aov(L_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.40 0.2011 0.451 0.638
## Residuals 165 73.52 0.4456print("L insula")## [1] "L insula"probe_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.345 0.1723 0.94 0.393
## Residuals 165 30.223 0.1832print("R dlPFC")## [1] "R dlPFC"probe_R_dlPFC.aov <- aov(R_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.58 0.2890 0.715 0.491
## Residuals 165 66.70 0.4043print("R Insula")## [1] "R Insula"probe_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.697 0.3484 2.011 0.137
## Residuals 165 28.577 0.1732print("R OFC")## [1] "R OFC"probe_R_OFC.aov <- aov(R_OFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_OFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.01 0.00255 0.013 0.987
## Residuals 165 32.53 0.19717FFA
No group differences in the FFA.
DFR_FFA_plots[["L_CUE_LE"]] + DFR_FFA_plots[["L_DELAY_LE"]] + DFR_FFA_plots[["L_PROBE_LE"]]+
plot_annotation(title="FFA during DFR task")
DFR_FFA_plots[["R_CUE_LE"]] + DFR_FFA_plots[["R_DELAY_LE"]] + DFR_FFA_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_FFA.aov <- aov(L_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_CUE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.58 0.7905 2.329 0.101
## Residuals 164 55.66 0.3394print("R Cue")## [1] "R Cue"R_CUE_LE_FFA.aov <- aov(R_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_CUE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.52 0.7612 2.431 0.0911 .
## Residuals 164 51.36 0.3131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Delay")## [1] "L Delay"L_DELAY_LE_FFA.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_DELAY_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.024 0.01221 0.286 0.752
## Residuals 164 7.010 0.04274print("R Delay")## [1] "R Delay"R_DELAY_LE_FFA.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_DELAY_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.019 0.00973 0.256 0.775
## Residuals 164 6.243 0.03807print("L Probe")## [1] "L Probe"L_PROBE_LE_FFA.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_PROBE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.21 1.1068 1.122 0.328
## Residuals 164 161.77 0.9864print("R Probe")## [1] "R Probe"R_PROBE_LE_FFA.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_PROBE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.34 1.1719 1.924 0.149
## Residuals 164 99.89 0.6091Scatter Plots
Some of these bar plots look pretty linear - let’s just check out a few scatter plots to see what they look like. The only significant one is for the L cue load effect.
data_for_plot <- merge(constructs_fMRI,p200_FFA)
ggplot(data = data_for_plot, aes(x=omnibus_span_no_DFR_MRI,y=L_CUE_LE))+
geom_point()+
geom_text(x=1,y=2,label="r=0.16*")+
stat_smooth(method="lm")
cor.test(data_for_plot$omnibus_span_no_DFR_MRI,data_for_plot$L_CUE_LE)##
## Pearson's product-moment correlation
##
## data: data_for_plot$omnibus_span_no_DFR_MRI and data_for_plot$L_CUE_LE
## t = 2.0817, df = 167, p-value = 0.0389
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.008271754 0.302725497
## sample estimates:
## cor
## 0.1590332HPC
Anterior
No differences in anterior HPC.
L3
DFR_HPC_Ant_plots[["L_CUE_L3"]] + DFR_HPC_Ant_plots[["L_DELAY_L3"]] + DFR_HPC_Ant_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_L3"]] + DFR_HPC_Ant_plots[["R_DELAY_L3"]] + DFR_HPC_Ant_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Ant.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.21 0.1042 0.452 0.637
## Residuals 164 37.79 0.2304print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Ant.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.01 0.00386 0.017 0.983
## Residuals 164 36.49 0.22249print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Ant.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.050 0.02496 0.799 0.452
## Residuals 164 5.124 0.03124print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Ant.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.111 0.05539 1.942 0.147
## Residuals 164 4.678 0.02853print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Ant.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.58 0.2895 0.567 0.568
## Residuals 164 83.71 0.5104print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Ant.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.03 0.0162 0.028 0.972
## Residuals 164 94.53 0.5764LE
DFR_HPC_Ant_plots[["L_CUE_LE"]] + DFR_HPC_Ant_plots[["L_DELAY_LE"]] + DFR_HPC_Ant_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_LE"]] + DFR_HPC_Ant_plots[["R_DELAY_LE"]] + DFR_HPC_Ant_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Ant.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.88 0.4421 2.286 0.105
## Residuals 164 31.71 0.1934print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Ant.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.31 0.1565 0.797 0.452
## Residuals 164 32.21 0.1964print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Ant.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.007 0.00346 0.095 0.91
## Residuals 164 5.984 0.03649print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Ant.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.000 0.00000 0 1
## Residuals 164 5.685 0.03466print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Ant.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.53 0.2642 0.441 0.644
## Residuals 164 98.18 0.5987print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Ant.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.50 0.2480 0.407 0.666
## Residuals 164 99.89 0.6091Medial
No differences in medial HPC.
L3
DFR_HPC_Med_plots[["L_CUE_L3"]] + DFR_HPC_Med_plots[["L_DELAY_L3"]] + DFR_HPC_Med_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_L3"]] + DFR_HPC_Med_plots[["R_DELAY_L3"]] + DFR_HPC_Med_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Med.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.057 0.02832 0.167 0.847
## Residuals 164 27.859 0.16987print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Med.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.165 0.0823 0.493 0.612
## Residuals 164 27.375 0.1669print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Med.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.064 0.03225 1.541 0.217
## Residuals 164 3.432 0.02093print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Med.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.107 0.05369 2.768 0.0657 .
## Residuals 164 3.180 0.01939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Med.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.78 0.3903 1.005 0.368
## Residuals 164 63.68 0.3883print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Med.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.44 0.2216 0.57 0.567
## Residuals 164 63.73 0.3886LE
DFR_HPC_Med_plots[["L_CUE_LE"]] + DFR_HPC_Med_plots[["L_DELAY_LE"]] + DFR_HPC_Med_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_LE"]] + DFR_HPC_Med_plots[["R_DELAY_LE"]] + DFR_HPC_Med_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Med.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.106 0.05281 0.446 0.641
## Residuals 164 19.402 0.11831print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Med.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.085 0.04242 0.33 0.719
## Residuals 164 21.074 0.12850print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Med.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.016 0.007803 0.314 0.731
## Residuals 164 4.080 0.024880print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Med.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.018 0.009052 0.406 0.667
## Residuals 164 3.652 0.022269print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Med.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.87 0.4375 1.03 0.359
## Residuals 164 69.62 0.4245print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Med.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.83 0.4133 1.134 0.324
## Residuals 164 59.77 0.3645Posterior
L3
L cue, L delay, L probe all show an inverted U-shaped relationship!
DFR_HPC_Post_plots[["L_CUE_L3"]] + DFR_HPC_Post_plots[["L_DELAY_L3"]] + DFR_HPC_Post_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_L3"]] + DFR_HPC_Post_plots[["R_DELAY_L3"]] + DFR_HPC_Post_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Post.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.897 0.4486 3.248 0.0413 *
## Residuals 164 22.649 0.1381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_CUE_L3_HPC_Post.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_CUE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.16960047 0.003492796 0.3357081 0.0441837
## low-high 0.03490477 -0.131956234 0.2017658 0.8739101
## low-med -0.13469570 -0.301556704 0.0321653 0.1392809print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Post.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.606 0.3032 2.242 0.11
## Residuals 164 22.184 0.1353print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Post.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1341 0.06703 5.062 0.00736 **
## Residuals 164 2.1718 0.01324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_DELAY_L3_HPC_Post.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_DELAY_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.04430651 -0.09574285 0.007129833 0.1065809
## low-high 0.02415245 -0.02751717 0.075822067 0.5119969
## low-med 0.06845896 0.01678934 0.120128577 0.0057712print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Post.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0605 0.03026 2.555 0.0808 .
## Residuals 164 1.9424 0.01184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Post.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.64 1.320 4.697 0.0104 *
## Residuals 164 46.09 0.281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_PROBE_L3_HPC_Post.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_PROBE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.1604192 -0.07652589 0.39736435 0.2479799
## low-high -0.1478909 -0.38591060 0.09012881 0.3082235
## low-med -0.3083101 -0.54632983 -0.07029042 0.0071781print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Post.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.49 0.7429 2.541 0.0819 .
## Residuals 164 47.94 0.2923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LE
However, no differences in the load effects.
DFR_HPC_Post_plots[["L_CUE_LE"]] + DFR_HPC_Post_plots[["L_DELAY_LE"]] + DFR_HPC_Post_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_LE"]] + DFR_HPC_Post_plots[["R_DELAY_LE"]] + DFR_HPC_Post_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Post.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.228 0.11410 1.169 0.313
## Residuals 164 16.002 0.09757print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Post.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.402 0.20113 2.349 0.0986 .
## Residuals 164 14.040 0.08561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Post.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.00 0.000007 0 1
## Residuals 164 2.99 0.018232print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Post.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0032 0.001594 0.117 0.89
## Residuals 164 2.2410 0.013665print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Post.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.73 0.8636 2.673 0.072 .
## Residuals 164 52.98 0.3230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Post.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.64 0.8192 2.83 0.0619 .
## Residuals 164 47.47 0.2895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Cortical Thickness
No significant differences
cortical_thickness_plots[["Cue_RH"]] + cortical_thickness_plots[["Delay_RH"]] + cortical_thickness_plots[["Probe_RH"]] +
plot_annotation(title="Cortical Thickness from DFR Full Mask") 
cortical_thickness_plots[["Cue_LH"]] + cortical_thickness_plots[["Delay_LH"]] + cortical_thickness_plots[["Probe_LH"]]
print("L Cue")## [1] "L Cue"L_CUE_DFR_thick.aov <- aov(Cue_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_CUE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0008 0.000394 0.044 0.957
## Residuals 164 1.4852 0.009056print("R Cue")## [1] "R Cue"R_CUE_DFR_thick.aov <- aov(Cue_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.402 0.20113 2.349 0.0986 .
## Residuals 164 14.040 0.08561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Delay")## [1] "L Delay"L_DELAY_DFR_thick.aov <- aov(Delay_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_DELAY_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.023 0.01153 0.51 0.601
## Residuals 163 3.681 0.02258
## 1 observation deleted due to missingnessprint("R Delay")## [1] "R Delay"R_DELAY_DFR_thick.aov <- aov(Delay_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_DELAY_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0017 0.000848 0.049 0.952
## Residuals 164 2.8501 0.017379print("L Probe")## [1] "L Probe"L_PROBE_DFR_thick.aov <- aov(Probe_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_PROBE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0402 0.02011 1.085 0.34
## Residuals 164 3.0398 0.01854print("R Probe")## [1] "R Probe"R_PROBE_DFR_thick.aov <- aov(Probe_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_PROBE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0054 0.002684 0.145 0.865
## Residuals 164 3.0418 0.018547Resting State Functional Connectivity
Within Network
No significant differences
RS_plots[["FPCN_FPCN"]] + RS_plots[["DMN_DMN"]] + RS_plots[["DAN_DAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Within Networks")
RS_plots[["VAN_VAN"]] + RS_plots[["CO_CO"]] + RS_plots[["visual_visual"]]
print("FPCN")## [1] "FPCN"FPCN.aov <- aov(FPCN_FPCN ~ level, data=split_RS[["all"]])
summary(FPCN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0075 0.003774 0.502 0.607
## Residuals 164 1.2342 0.007526print("DMN")## [1] "DMN"DMN.aov <- aov(DMN_DMN ~ level, data=split_RS[["all"]])
summary(DMN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0073 0.003628 0.383 0.683
## Residuals 164 1.5545 0.009479print("DAN")## [1] "DAN"DAN.aov <- aov(DAN_DAN ~ level, data=split_RS[["all"]])
summary(DAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0001 0.000040 0.005 0.995
## Residuals 164 1.3073 0.007972print("VAN")## [1] "VAN"VAN.aov <- aov(VAN_VAN ~ level, data=split_RS[["all"]])
summary(VAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0497 0.024840 2.838 0.0614 .
## Residuals 164 1.4353 0.008752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("CO")## [1] "CO"CO.aov <- aov(CO_CO ~ level, data=split_RS[["all"]])
summary(CO.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0117 0.005838 0.715 0.491
## Residuals 164 1.3383 0.008160print("CO")## [1] "CO"visual.aov <- aov(visual_visual ~ level, data=split_RS[["all"]])
summary(visual.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.008 0.003997 0.232 0.794
## Residuals 164 2.831 0.017260Across Network
Differences in FPCN/CO connectivity, with low > medium
RS_plots[["FPCN_DMN"]] + RS_plots[["FPCN_DAN"]] + RS_plots[["FCPN_VAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Across Networks")
RS_plots[["FPCN_CO"]] + RS_plots[["FPCN_visual"]]
print("FPCN DMN")## [1] "FPCN DMN"FPCN_DMN.aov <- aov(FPCN_DMN ~ level, data=split_RS[["all"]])
summary(FPCN_DMN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0171 0.008558 1.069 0.346
## Residuals 164 1.3135 0.008009print("FPCN DAN")## [1] "FPCN DAN"FPCN_DAN.aov <- aov(FPCN_DAN ~ level, data=split_RS[["all"]])
summary(FPCN_DAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0161 0.008045 1.577 0.21
## Residuals 164 0.8365 0.005100print("FPCN VAN")## [1] "FPCN VAN"FPCN_VAN.aov <- aov(FPCN_VAN ~ level, data=split_RS[["all"]])
summary(FPCN_VAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0099 0.004963 0.719 0.489
## Residuals 164 1.1316 0.006900print("FPCN CO")## [1] "FPCN CO"FPCN_CO.aov <- aov(FPCN_CO ~ level, data=split_RS[["all"]])
summary(FPCN_CO.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0632 0.03159 3.124 0.0466 *
## Residuals 164 1.6582 0.01011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(FPCN_CO.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_CO ~ level, data = split_RS[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.01367667 -0.058621945 0.03126860 0.7521826
## low-high 0.03279388 -0.012355227 0.07794299 0.2015508
## low-med 0.04647055 0.001321446 0.09161966 0.0420669print("FPCN visual")## [1] "FPCN visual"FPCN_visual.aov <- aov(FPCN_visual ~ level, data=split_RS[["all"]])
summary(FPCN_visual.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0251 0.012527 1.709 0.184
## Residuals 164 1.2025 0.007332Beta Series Connectivity
Cue Period
High Load
No differences
beta_conn_cue_plots[["FPCN_FPCN_L3"]] + beta_conn_cue_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_cue_plots[["FPCN_FFA_L3"]] + beta_conn_cue_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_cue_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.043 0.02166 0.163 0.849
## Residuals 164 21.737 0.13254FPCN_HPC_BC_cue_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.167 0.0833 0.761 0.469
## Residuals 164 17.940 0.1094FPCN_FFA_BC_cue_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.161 0.08058 0.74 0.479
## Residuals 164 17.852 0.10885HPC_FFA_BC_cue_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.087 0.04347 0.431 0.651
## Residuals 164 16.557 0.10096Load Effect
No differences
beta_conn_cue_plots[["FPCN_FPCN_LE"]] + beta_conn_cue_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_cue_plots[["FPCN_FFA_LE"]] + beta_conn_cue_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_cue_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.487 0.2437 1.575 0.21
## Residuals 164 25.367 0.1547FPCN_HPC_BC_cue_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.188 0.09425 0.838 0.434
## Residuals 164 18.443 0.11246FPCN_FFA_BC_cue_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.27 0.1348 0.764 0.467
## Residuals 164 28.92 0.1764HPC_FFA_BC_cue_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.202 0.1008 0.634 0.532
## Residuals 164 26.072 0.1590Delay Period
High Load
No differences
beta_conn_delay_plots[["FPCN_FPCN_L3"]] + beta_conn_delay_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_delay_plots[["FPCN_FFA_L3"]] + beta_conn_delay_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_delay_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.079 0.03926 0.374 0.688
## Residuals 164 17.207 0.10492FPCN_HPC_BC_delay_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.17 0.08503 1.157 0.317
## Residuals 164 12.05 0.07349FPCN_FFA_BC_delay_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.028 0.01405 0.236 0.79
## Residuals 164 9.745 0.05942HPC_FFA_BC_delay_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.182 0.09079 1.403 0.249
## Residuals 164 10.612 0.06470Load Effect
Significant difference in the HPC/FFA load effect connectivity, med > low
beta_conn_delay_plots[["FPCN_FPCN_LE"]] + beta_conn_delay_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_delay_plots[["FPCN_FFA_LE"]] + beta_conn_delay_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_delay_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.087 0.04339 0.939 0.393
## Residuals 164 7.579 0.04621FPCN_HPC_BC_delay_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.12 0.06010 0.96 0.385
## Residuals 164 10.27 0.06261FPCN_FFA_BC_delay_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.134 0.06693 0.967 0.382
## Residuals 164 11.353 0.06923HPC_FFA_BC_delay_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.759 0.3795 4.477 0.0128 *
## Residuals 164 13.905 0.0848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(HPC_FFA_BC_delay_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.08828824 -0.04186248 0.21843895 0.2466493
## low-high -0.07694390 -0.20768488 0.05379707 0.3474521
## low-med -0.16523214 -0.29597311 -0.03449117 0.0090113BCT Measures
Overall Measures
Significant differences in mean participation coefficient, with low > med.
BCT_plots[["Participation_Coef_Mean"]] + BCT_plots[["Global_Eff"]] + BCT_plots[["Modularity_Louvain_N"]]+
plot_annotation(title="Overall BCT Measures")
print("Mean Participation Coefficient")## [1] "Mean Participation Coefficient"partic_coef_mean.aov <- aov(Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
summary(partic_coef_mean.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.50 0.25019 3.589 0.0298 *
## Residuals 164 11.43 0.06971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(partic_coef_mean.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.09476071 -0.21277299 0.02325156 0.1421466
## low-high 0.03477253 -0.08377494 0.15332001 0.7674681
## low-med 0.12953325 0.01098577 0.24808072 0.0285001print("Global Efficiency")## [1] "Global Efficiency"global_eff.aov <- aov(Global_Eff ~ level, data = split_BCT[["all"]])
summary(global_eff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0116 0.005797 0.791 0.455
## Residuals 164 1.2013 0.007325print("Modularity")## [1] "Modularity"modularity.aov <- aov(Modularity_Louvain_N ~ level, data = split_BCT[["all"]])
summary(modularity.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.5 1.756 0.247 0.782
## Residuals 164 1166.3 7.111Individual Networks
DMN: low > medium VAN: low > high, low > medium
indiv_partic_coeff_plots[["FrontoParietal"]] + indiv_partic_coeff_plots[["Default"]] + indiv_partic_coeff_plots[["DorsalAttn"]]+
plot_annotation(title="Individual Network Participation Coefficient")
indiv_partic_coeff_plots[["CinguloOperc"]] + indiv_partic_coeff_plots[["VentralAttn"]] + indiv_partic_coeff_plots[["Visual"]]
print("FPCN")## [1] "FPCN"FPCN_indiv_coeff.aov <- aov(FrontoParietal ~ level, data = split_indiv_partic_coeff[["all"]])
summary(FPCN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.529 0.2647 1.492 0.228
## Residuals 164 29.101 0.1774print("DMN")## [1] "DMN"DMN_indiv_coeff.aov <- aov(Default ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DMN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.577 0.7885 4.559 0.0118 *
## Residuals 164 28.366 0.1730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(DMN_indiv_coeff.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Default ~ level, data = split_indiv_partic_coeff[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.16531589 -0.35120912 0.02057734 0.0922688
## low-high 0.06568638 -0.12104990 0.25242267 0.6837361
## low-med 0.23100227 0.04426599 0.41773856 0.0108914print("DAN")## [1] "DAN"DAN_indiv_coeff.aov <- aov(DorsalAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DAN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.11 0.05497 0.636 0.531
## Residuals 164 14.18 0.08647print("CO")## [1] "CO"CO_indiv_coeff.aov <- aov(CinguloOperc ~ level, data = split_indiv_partic_coeff[["all"]])
summary(CO_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.691 0.3457 2.991 0.053 .
## Residuals 164 18.958 0.1156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("VAN")## [1] "VAN"VAN_indiv_coeff.aov <- aov(VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(VAN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.329 0.6645 6.072 0.00286 **
## Residuals 164 17.947 0.1094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(VAN_indiv_coeff.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.05493054 -0.202792101 0.09293101 0.6545113
## low-high 0.15621078 0.007678649 0.30474292 0.0367404
## low-med 0.21114133 0.062609192 0.35967346 0.0027547print("visual")## [1] "visual"visual_indiv_coeff.aov <- aov(Visual ~ level, data = split_indiv_partic_coeff[["all"]])
summary(visual_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.749 0.3744 2.179 0.116
## Residuals 164 28.181 0.1718Scatter Plots
The VAN looks like it might be linear – let’s just quickly look at the scatter plot to see what that looks like.
data_for_plot <- merge(constructs_fMRI,p200_indiv_network_ParticCoeff)
ggplot(data = data_for_plot, aes(x=omnibus_span_no_DFR_MRI,y=VentralAttn))+
geom_point()+
geom_text(x=1,y=2.4,label="r=-0.17*")+
stat_smooth(method="lm")
cor.test(data_for_plot$omnibus_span_no_DFR_MRI,data_for_plot$VentralAttn)##
## Pearson's product-moment correlation
##
## data: data_for_plot$omnibus_span_no_DFR_MRI and data_for_plot$VentralAttn
## t = -2.1999, df = 167, p-value = 0.02919
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.31090010 -0.01729437
## sample estimates:
## cor
## -0.1678163Moderation Analyses
Another way to investigate these relationships relies on moderation analyses, where we can look at the relationship between two variables (such as BPRS and load related activity) at different levels of capacity (ie mean, +1 SD, -1 SD).
Also, none of these variables show any significant interactions, so we can’t look into moderation. We should think more about which variables we want to be looking at.
# get data we're curious about
data_for_moderation <- data.frame(split_clinical[["all"]][c(1,8)])
data_for_moderation <- merge(data_for_moderation,split_constructs[["all"]][c(1,7)],by="PTID")
data_for_moderation <- merge(data_for_moderation,split_DFR_delay[["all"]][c(1,15)],by="PTID")
data_for_moderation <- merge(data_for_moderation,p200_data[,c(1,7,8)],by="PTID")
data_for_moderation <- merge(data_for_moderation,p200_demographics,by="PTID")
data_for_moderation <- merge(data_for_moderation,p200_beta_conn_cue[,c(1,3:4,6:7,12:13)],by="PTID")
data_for_moderation <- merge(data_for_moderation,p200_beta_conn_delay[,c(1,3:4,6:7,9:10,12:13)],by="PTID")
# center the data
data_for_moderation$omnibus_c <- c(scale(data_for_moderation$omnibus_span_no_DFR_MRI, center=TRUE,scale=FALSE))
data_for_moderation$activity_c <- c(scale(data_for_moderation$DFR_ROIs,center=TRUE,scale=FALSE))
data_for_moderation$BPRS_c <- c(scale(data_for_moderation$BPRS_TOT,center=TRUE,scale=FALSE))
data_for_moderation$HPC_FFA_cue_L3_c <- c(scale(data_for_moderation$HPC_FFA_L3.x,center=TRUE,scale=FALSE))
data_for_moderation$FPCN_HPC_cue_L3_c <- c(scale(data_for_moderation$FPCN_HPC_L3.x,center=TRUE,scale=FALSE))
data_for_moderation$FPCN_HPC_delay_L3_c <- c(scale(data_for_moderation$FPCN_HPC_L3.y,center=TRUE,scale=FALSE))
data_for_moderation$FPCN_FFA_delay_L3_c <- c(scale(data_for_moderation$FPCN_FFA_L3,center=TRUE,scale=FALSE))
data_for_moderation$FPCN_FPCN_delay_L3_c <- c(scale(data_for_moderation$FPCN_FPCN_L3.y,center=TRUE,scale=FALSE))DFR_ROIs_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + activity_c + GENDER + AGE + SCANNER + omnibus_c * activity_c, data=data_for_moderation)
summary(DFR_ROIs_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + activity_c + GENDER +
## AGE + SCANNER + omnibus_c * activity_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28398 -0.05926 0.01437 0.05748 0.22224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7388171 0.0542379 13.622 < 2e-16 ***
## omnibus_c 0.0516012 0.0148128 3.484 0.00064 ***
## activity_c 0.0409289 0.0177195 2.310 0.02219 *
## GENDER 0.0153235 0.0121288 1.263 0.20831
## AGE -0.0005784 0.0015451 -0.374 0.70864
## SCANNER -0.0090991 0.0157379 -0.578 0.56398
## omnibus_c:activity_c 0.0238631 0.0400472 0.596 0.55211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09657 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1399, Adjusted R-squared: 0.1072
## F-statistic: 4.283 on 6 and 158 DF, p-value: 0.0004988# ps <- plotSlopes(DFR_ROIs_span_mod, plotx="activity_c", modx="omnibus_c", xlab = "DFR ROIs", ylab = "DFR L3 Accuracy", modxVals = "std.dev",interval="confidence")
#
# plot(testSlopes(ps))
BPRS_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + BPRS_c + GENDER + AGE + SCANNER + omnibus_c * BPRS_c, data=data_for_moderation)
summary(BPRS_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + BPRS_c + GENDER +
## AGE + SCANNER + omnibus_c * BPRS_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.268681 -0.064148 0.007451 0.066195 0.253485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7600824 0.0537099 14.152 < 2e-16 ***
## omnibus_c 0.0479000 0.0149528 3.203 0.00164 **
## BPRS_c -0.0140086 0.0085587 -1.637 0.10367
## GENDER 0.0199827 0.0122121 1.636 0.10377
## AGE -0.0005078 0.0015105 -0.336 0.73716
## SCANNER -0.0278760 0.0162050 -1.720 0.08735 .
## omnibus_c:BPRS_c 0.0225711 0.0134810 1.674 0.09605 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09631 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1445, Adjusted R-squared: 0.112
## F-statistic: 4.447 on 6 and 158 DF, p-value: 0.000347activity_on_BPRS_span_mod <- lm(BPRS_TOT ~ omnibus_c + activity_c + GENDER + AGE + SCANNER + omnibus_c * activity_c, data=data_for_moderation)
summary(activity_on_BPRS_span_mod)##
## Call:
## lm(formula = BPRS_TOT ~ omnibus_c + activity_c + GENDER + AGE +
## SCANNER + omnibus_c * activity_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5678 -0.6634 -0.0712 0.4727 4.2526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.629361 0.519360 1.212 0.227398
## omnibus_c -0.514371 0.141841 -3.626 0.000387 ***
## activity_c -0.047541 0.169674 -0.280 0.779697
## GENDER 0.024973 0.116141 0.215 0.830024
## AGE 0.009523 0.014796 0.644 0.520722
## SCANNER -0.660564 0.150699 -4.383 2.12e-05 ***
## omnibus_c:activity_c -0.489896 0.383476 -1.278 0.203295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9247 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1713, Adjusted R-squared: 0.1399
## F-statistic: 5.444 on 6 and 158 DF, p-value: 3.828e-05HPC_FFA_on_acc_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + HPC_FFA_cue_L3_c + GENDER + AGE + SCANNER + omnibus_c * HPC_FFA_cue_L3_c, data=data_for_moderation)
summary(HPC_FFA_on_acc_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + HPC_FFA_cue_L3_c +
## GENDER + AGE + SCANNER + omnibus_c * HPC_FFA_cue_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.278987 -0.061447 0.009666 0.068040 0.221053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7669562 0.0537957 14.257 < 2e-16 ***
## omnibus_c 0.0541288 0.0147899 3.660 0.000344 ***
## HPC_FFA_cue_L3_c -0.0631529 0.0239777 -2.634 0.009282 **
## GENDER 0.0126125 0.0122780 1.027 0.305876
## AGE -0.0009095 0.0015135 -0.601 0.548771
## SCANNER -0.0180826 0.0155783 -1.161 0.247493
## omnibus_c:HPC_FFA_cue_L3_c -0.0409245 0.0430318 -0.951 0.343042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09635 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1438, Adjusted R-squared: 0.1113
## F-statistic: 4.422 on 6 and 158 DF, p-value: 0.0003669FPCN_HPC_on_acc_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_HPC_cue_L3_c + GENDER + AGE + SCANNER + omnibus_c * FPCN_HPC_cue_L3_c, data=data_for_moderation)
summary(FPCN_HPC_on_acc_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_HPC_cue_L3_c +
## GENDER + AGE + SCANNER + omnibus_c * FPCN_HPC_cue_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28013 -0.06580 0.01409 0.07020 0.22723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7606002 0.0540275 14.078 < 2e-16 ***
## omnibus_c 0.0549943 0.0151708 3.625 0.000389 ***
## FPCN_HPC_cue_L3_c -0.0491405 0.0237749 -2.067 0.040374 *
## GENDER 0.0138246 0.0123800 1.117 0.265826
## AGE -0.0007275 0.0015281 -0.476 0.634702
## SCANNER -0.0186760 0.0156042 -1.197 0.233156
## omnibus_c:FPCN_HPC_cue_L3_c -0.0224034 0.0455629 -0.492 0.623612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09728 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1273, Adjusted R-squared: 0.09413
## F-statistic: 3.84 on 6 and 158 DF, p-value: 0.001329FPCN_HPC_delay_on_acc_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_HPC_delay_L3_c + GENDER + AGE + SCANNER + omnibus_c * FPCN_HPC_delay_L3_c, data=data_for_moderation)
summary(FPCN_HPC_delay_on_acc_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_HPC_delay_L3_c +
## GENDER + AGE + SCANNER + omnibus_c * FPCN_HPC_delay_L3_c,
## data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.278514 -0.061755 0.008675 0.077511 0.236800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7514433 0.0535941 14.021 < 2e-16 ***
## omnibus_c 0.0586983 0.0152551 3.848 0.000173 ***
## FPCN_HPC_delay_L3_c -0.0725363 0.0286706 -2.530 0.012386 *
## GENDER 0.0131886 0.0123028 1.072 0.285355
## AGE -0.0007675 0.0015160 -0.506 0.613382
## SCANNER -0.0107878 0.0156810 -0.688 0.492491
## omnibus_c:FPCN_HPC_delay_L3_c -0.0025414 0.0610179 -0.042 0.966830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09664 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1387, Adjusted R-squared: 0.106
## F-statistic: 4.24 on 6 and 158 DF, p-value: 0.0005489FPCN_FFA_delay_on_acc_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_FFA_delay_L3_c + GENDER + AGE + SCANNER + omnibus_c * FPCN_FFA_delay_L3_c, data=data_for_moderation)
summary(FPCN_FFA_delay_on_acc_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_FFA_delay_L3_c +
## GENDER + AGE + SCANNER + omnibus_c * FPCN_FFA_delay_L3_c,
## data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.265451 -0.066490 0.008384 0.067418 0.228807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7569645 0.0541020 13.991 < 2e-16 ***
## omnibus_c 0.0527711 0.0151470 3.484 0.000639 ***
## FPCN_FFA_delay_L3_c -0.0516113 0.0320988 -1.608 0.109856
## GENDER 0.0120379 0.0125065 0.963 0.337251
## AGE -0.0005606 0.0015317 -0.366 0.714876
## SCANNER -0.0171871 0.0157507 -1.091 0.276848
## omnibus_c:FPCN_FFA_delay_L3_c -0.0618868 0.0685748 -0.902 0.368180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09759 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1216, Adjusted R-squared: 0.08821
## F-statistic: 3.644 on 6 and 158 DF, p-value: 0.002046FPCN_FPCN_delay_on_acc_span_mod <- lm(XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_FPCN_delay_L3_c + GENDER + AGE + SCANNER + omnibus_c * FPCN_FPCN_delay_L3_c, data=data_for_moderation)
summary(FPCN_FPCN_delay_on_acc_span_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ omnibus_c + FPCN_FPCN_delay_L3_c +
## GENDER + AGE + SCANNER + omnibus_c * FPCN_FPCN_delay_L3_c,
## data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.277904 -0.068307 0.006965 0.069322 0.220572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7571343 0.0544469 13.906 < 2e-16 ***
## omnibus_c 0.0545618 0.0147046 3.711 0.000286 ***
## FPCN_FPCN_delay_L3_c -0.0265309 0.0241744 -1.097 0.274102
## GENDER 0.0154084 0.0124518 1.237 0.217760
## AGE -0.0007249 0.0015424 -0.470 0.638998
## SCANNER -0.0180996 0.0156819 -1.154 0.250174
## omnibus_c:FPCN_FPCN_delay_L3_c -0.0430554 0.0477512 -0.902 0.368609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09788 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1165, Adjusted R-squared: 0.08294
## F-statistic: 3.472 on 6 and 158 DF, p-value: 0.002989DFR_ROIs_BPRS_mod <- lm(XDFR_MRI_ACC_L3 ~ BPRS_c + activity_c + GENDER + AGE + SCANNER + BPRS_c * activity_c, data=data_for_moderation)
summary(DFR_ROIs_BPRS_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ BPRS_c + activity_c + GENDER +
## AGE + SCANNER + BPRS_c * activity_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28072 -0.06155 0.01075 0.06353 0.26868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7852563 0.0532797 14.738 < 2e-16 ***
## BPRS_c -0.0232099 0.0081277 -2.856 0.00487 **
## activity_c 0.0487894 0.0174704 2.793 0.00587 **
## GENDER 0.0104354 0.0121133 0.861 0.39028
## AGE -0.0009835 0.0015157 -0.649 0.51737
## SCANNER -0.0270274 0.0166883 -1.620 0.10732
## BPRS_c:activity_c -0.0032943 0.0185217 -0.178 0.85906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09771 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1195, Adjusted R-squared: 0.08602
## F-statistic: 3.572 on 6 and 158 DF, p-value: 0.002398HPC_FFA_on_acc_BPRS_mod <- lm(XDFR_MRI_ACC_L3 ~ BPRS_c + HPC_FFA_cue_L3_c + GENDER + AGE + SCANNER + BPRS_c * HPC_FFA_cue_L3_c, data=data_for_moderation)
summary(HPC_FFA_on_acc_BPRS_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ BPRS_c + HPC_FFA_cue_L3_c + GENDER +
## AGE + SCANNER + BPRS_c * HPC_FFA_cue_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.27373 -0.07300 0.01462 0.07486 0.27193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.820182 0.052806 15.532 < 2e-16 ***
## BPRS_c -0.024422 0.008140 -3.000 0.00314 **
## HPC_FFA_cue_L3_c -0.057343 0.026149 -2.193 0.02977 *
## GENDER 0.007752 0.012351 0.628 0.53115
## AGE -0.001608 0.001534 -1.048 0.29614
## SCANNER -0.036167 0.016338 -2.214 0.02828 *
## BPRS_c:HPC_FFA_cue_L3_c 0.008182 0.019951 0.410 0.68228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09857 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.104, Adjusted R-squared: 0.06994
## F-statistic: 3.055 on 6 and 158 DF, p-value: 0.007426FPCN_HPC_on_acc_BPRS_mod <- lm(XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_HPC_cue_L3_c + GENDER + AGE + SCANNER + BPRS_c * FPCN_HPC_cue_L3_c, data=data_for_moderation)
summary(FPCN_HPC_on_acc_BPRS_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_HPC_cue_L3_c + GENDER +
## AGE + SCANNER + BPRS_c * FPCN_HPC_cue_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.275500 -0.082966 0.009841 0.074495 0.276040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.815584 0.053259 15.314 < 2e-16 ***
## BPRS_c -0.025067 0.008163 -3.071 0.00251 **
## FPCN_HPC_cue_L3_c -0.043708 0.023821 -1.835 0.06840 .
## GENDER 0.008109 0.012329 0.658 0.51169
## AGE -0.001351 0.001549 -0.872 0.38468
## SCANNER -0.038329 0.016376 -2.341 0.02051 *
## BPRS_c:FPCN_HPC_cue_L3_c 0.012474 0.019351 0.645 0.52010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09903 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.09563, Adjusted R-squared: 0.06129
## F-statistic: 2.785 on 6 and 158 DF, p-value: 0.01333FPCN_HPC_delay_on_acc_BPRS_mod <- lm(XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_HPC_delay_L3_c + GENDER + AGE + SCANNER + BPRS_c * FPCN_HPC_delay_L3_c, data=data_for_moderation)
summary(FPCN_HPC_delay_on_acc_BPRS_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_HPC_delay_L3_c +
## GENDER + AGE + SCANNER + BPRS_c * FPCN_HPC_delay_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.271516 -0.072592 0.005912 0.069130 0.271164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.811022 0.052055 15.580 < 2e-16 ***
## BPRS_c -0.025109 0.008092 -3.103 0.00227 **
## FPCN_HPC_delay_L3_c -0.073331 0.028834 -2.543 0.01195 *
## GENDER 0.006121 0.012134 0.504 0.61464
## AGE -0.001337 0.001506 -0.887 0.37623
## SCANNER -0.031712 0.016262 -1.950 0.05294 .
## BPRS_c:FPCN_HPC_delay_L3_c 0.045698 0.026360 1.734 0.08493 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09733 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1263, Adjusted R-squared: 0.09312
## F-statistic: 3.807 on 6 and 158 DF, p-value: 0.001431FPCN_FFA_delay_on_acc_BPRS_mod <- lm(XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_FFA_delay_L3_c + GENDER + AGE + SCANNER + BPRS_c * FPCN_FFA_delay_L3_c, data=data_for_moderation)
summary(FPCN_FFA_delay_on_acc_BPRS_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_FFA_delay_L3_c +
## GENDER + AGE + SCANNER + BPRS_c * FPCN_FFA_delay_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.257472 -0.068571 0.003392 0.069671 0.270075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.813866 0.052550 15.488 < 2e-16 ***
## BPRS_c -0.024365 0.008241 -2.956 0.00359 **
## FPCN_FFA_delay_L3_c -0.067058 0.032922 -2.037 0.04333 *
## GENDER 0.005961 0.012368 0.482 0.63048
## AGE -0.001194 0.001520 -0.785 0.43347
## SCANNER -0.036567 0.016287 -2.245 0.02615 *
## BPRS_c:FPCN_FFA_delay_L3_c 0.046139 0.027956 1.650 0.10084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09829 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.109, Adjusted R-squared: 0.07516
## F-statistic: 3.221 on 6 and 158 DF, p-value: 0.005175FPCN_FPCN_delay_on_acc_BPRS_mod <- lm(XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_FPCN_delay_L3_c + GENDER + AGE + SCANNER + BPRS_c * FPCN_FPCN_delay_L3_c, data=data_for_moderation)
summary(FPCN_FPCN_delay_on_acc_BPRS_mod)##
## Call:
## lm(formula = XDFR_MRI_ACC_L3 ~ BPRS_c + FPCN_FPCN_delay_L3_c +
## GENDER + AGE + SCANNER + BPRS_c * FPCN_FPCN_delay_L3_c, data = data_for_moderation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.27126 -0.07008 0.01060 0.07187 0.25973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.817261 0.053534 15.266 < 2e-16 ***
## BPRS_c -0.024184 0.008321 -2.906 0.00418 **
## FPCN_FPCN_delay_L3_c -0.026449 0.024786 -1.067 0.28757
## GENDER 0.008383 0.012446 0.674 0.50158
## AGE -0.001554 0.001541 -1.008 0.31483
## SCANNER -0.036019 0.016588 -2.171 0.03139 *
## BPRS_c:FPCN_FPCN_delay_L3_c -0.017134 0.026529 -0.646 0.51931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09952 on 158 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.08649, Adjusted R-squared: 0.0518
## F-statistic: 2.493 on 6 and 158 DF, p-value: 0.02479